Using ERIC to Assess Implementation Science in Drowning Prevention Interventions in High-Income Countries: A Systematic Review
Bibliographic record
Abstract
This systematic review identifies and describes the use of the Expert Recommendation for Implementing Change (ERIC) concepts and strategies using public health approaches to drowning prevention interventions as a case study. International calls for action have identified the need to better understand the implementation of drowning prevention interventions so that intervention design and implementation is improved. In high-income countries (HICs), interventions are sophisticated but still little is known or written about their implementation. The review was registered on PROSPERO (number CRD42022347789) and followed the PRISMA guidelines. Eight databases were searched. Articles were assessed using the Public Health Ontario Meta-tool for quality appraisal of public health evidence. Forty-nine articles were included. Where ERIC strategies were reported, the focus was on evaluative and iterative strategies, developing partnerships and engaging the target group. The review identified few articles that discussed intervention development and implementation sufficiently for strategies to be replicated. Findings will inform further research into the use and measurement of implementation strategies by practitioners and researchers undertaking work in drowning prevention in HICs and supports a call to action for better documentation of implementation in public health interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.113 | 0.303 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".